Atividade Final
Letícia Maria Evangelista de Souza
Dactylopterus volitans
O estudo de diversas espécies pode ser feito por meio de repositórios, pacotes e funções na linguagem R. Vamos testar?
Para começar precisamos escolher a espécie que vamos analisar. Que tal a Dactylopterus volitans, conhecido como peixe-voador? O nome popular peixe-voador se deve a suas nadadeiras parecerem asas quando abertas. O peixe-voador é uma espécie marinha e estuariana associada a recifes e sua distibuição se estende por todo o Atlântico, do Canadá a Argentina, e do outro lado do Atlântico, se estende pela costa do continente Africano e no Mediterrâneo. Caso você queira mais informações sobre o peixe-voador acesse o FishBase.
Dactylopterus volitans - FishBase
Acesso e limpeza de dados pelo GBIF e OBIS
GBIF
Primeiro, vamos extrair os dados dos repositórios GBIF e OBIS com os pacotes rgbif e
robis. Depois vamos usar o pacote tidyverse, para carregar os
demais pacotes, como o dplyr, para manipularmos dados, e o
ggplot, para visualizarmos dados. Agora vamos carregar o
pacote rgbif pois
ele permite o acesso aos dados do GBIF. O pacote rgbif
possui a função occ_data, função essa que nos permite
buscar dados das espécies pelo nome científico, numeração ou local. O
nosso script estará parecido com esse abaixo:
library(tidyverse)
library(rgbif)
flying_gbif <- occ_data(scientificName = "Dactylopterus volitans",
hasCoordinate = TRUE,
hasGeospatialIssue=FALSE)Agora nós vamos começar a limpar os dados com as funções
issue e strsplit, a primeira função nos
permite identificar os possíveis erros e a segunda nos permite
individualizar variáveis duplicadas. Os comandos e output no
script ficarão como abaixo, em que o carácter $ nos indica que estamos a
acessar os data frames baixados do GBIF. Além disso, vamos utilizar a
função dim para checar as variáveis, essa função nos
permite checar a dimensão dos dados retornados pelo repositório.
# dimensoes
dim(flying_gbif)## NULLdim(flying_gbif$data)## [1] 500 152# checar campos
flying_gbif$data %>% names## [1] "key" "scientificName"
## [3] "decimalLatitude" "decimalLongitude"
## [5] "issues" "datasetKey"
## [7] "publishingOrgKey" "installationKey"
## [9] "publishingCountry" "protocol"
## [11] "lastCrawled" "lastParsed"
## [13] "crawlId" "hostingOrganizationKey"
## [15] "basisOfRecord" "occurrenceStatus"
## [17] "lifeStage" "taxonKey"
## [19] "kingdomKey" "phylumKey"
## [21] "classKey" "orderKey"
## [23] "familyKey" "genusKey"
## [25] "speciesKey" "acceptedTaxonKey"
## [27] "acceptedScientificName" "kingdom"
## [29] "phylum" "order"
## [31] "family" "genus"
## [33] "species" "genericName"
## [35] "specificEpithet" "taxonRank"
## [37] "taxonomicStatus" "iucnRedListCategory"
## [39] "dateIdentified" "coordinateUncertaintyInMeters"
## [41] "stateProvince" "year"
## [43] "month" "day"
## [45] "eventDate" "modified"
## [47] "lastInterpreted" "references"
## [49] "license" "isInCluster"
## [51] "datasetName" "recordedBy"
## [53] "identifiedBy" "inCluster"
## [55] "geodeticDatum" "class"
## [57] "countryCode" "country"
## [59] "rightsHolder" "identifier"
## [61] "http://unknown.org/nick" "verbatimEventDate"
## [63] "gbifID" "verbatimLocality"
## [65] "collectionCode" "occurrenceID"
## [67] "taxonID" "catalogNumber"
## [69] "institutionCode" "eventTime"
## [71] "occurrenceRemarks" "http://unknown.org/captive"
## [73] "identificationID" "identificationRemarks"
## [75] "depth" "depthAccuracy"
## [77] "http://unknown.org/language" "locality"
## [79] "http://unknown.org/rights" "taxonConceptID"
## [81] "http://unknown.org/rightsHolder" "associatedSequences"
## [83] "datasetID" "eventID"
## [85] "footprintWKT" "county"
## [87] "originalNameUsage" "identificationVerificationStatus"
## [89] "nameAccordingTo" "networkKeys"
## [91] "individualCount" "elevation"
## [93] "elevationAccuracy" "recordNumber"
## [95] "municipality" "language"
## [97] "type" "ownerInstitutionCode"
## [99] "continent" "waterBody"
## [101] "habitat" "institutionID"
## [103] "parentEventID" "footprintSRS"
## [105] "sex" "establishmentMeans"
## [107] "organismQuantity" "organismQuantityType"
## [109] "institutionKey" "collectionKey"
## [111] "preparations" "samplingProtocol"
## [113] "nomenclaturalCode" "higherGeography"
## [115] "georeferenceVerificationStatus" "endDayOfYear"
## [117] "fieldNumber" "verbatimDepth"
## [119] "locationRemarks" "startDayOfYear"
## [121] "accessRights" "bibliographicCitation"
## [123] "higherClassification" "collectionID"
## [125] "rights" "georeferenceSources"
## [127] "projectId" "islandGroup"
## [129] "samplingEffort" "locationAccordingTo"
## [131] "higherGeographyID" "georeferencedDate"
## [133] "georeferencedBy" "island"
## [135] "georeferenceProtocol" "verbatimSRS"
## [137] "verbatimCoordinateSystem" "georeferenceRemarks"
## [139] "locationID" "taxonRemarks"
## [141] "disposition" "vernacularName"
## [143] "fieldNotes" "organismName"
## [145] "materialSampleID" "dynamicProperties"
## [147] "namePublishedInYear" "acceptedNameUsage"
## [149] "parentNameUsage" "otherCatalogNumbers"
## [151] "eventRemarks" "name"#problemas indentificados pelo repositório
gbif_issues()## code issue
## 1 bri BASIS_OF_RECORD_INVALID
## 2 ccm CONTINENT_COUNTRY_MISMATCH
## 3 cdc CONTINENT_DERIVED_FROM_COORDINATES
## 4 conti CONTINENT_INVALID
## 5 cdiv COORDINATE_INVALID
## 6 cdout COORDINATE_OUT_OF_RANGE
## 7 cdrep COORDINATE_REPROJECTED
## 8 cdrepf COORDINATE_REPROJECTION_FAILED
## 9 cdreps COORDINATE_REPROJECTION_SUSPICIOUS
## 10 cdround COORDINATE_ROUNDED
## 11 cucdmis COUNTRY_COORDINATE_MISMATCH
## 12 cudc COUNTRY_DERIVED_FROM_COORDINATES
## 13 cuiv COUNTRY_INVALID
## 14 cum COUNTRY_MISMATCH
## 15 depmms DEPTH_MIN_MAX_SWAPPED
## 16 depnn DEPTH_NON_NUMERIC
## 17 depnmet DEPTH_NOT_METRIC
## 18 depunl DEPTH_UNLIKELY
## 19 elmms ELEVATION_MIN_MAX_SWAPPED
## 20 elnn ELEVATION_NON_NUMERIC
## 21 elnmet ELEVATION_NOT_METRIC
## 22 elunl ELEVATION_UNLIKELY
## 23 gass84 GEODETIC_DATUM_ASSUMED_WGS84
## 24 gdativ GEODETIC_DATUM_INVALID
## 25 iddativ IDENTIFIED_DATE_INVALID
## 26 iddatunl IDENTIFIED_DATE_UNLIKELY
## 27 mdativ MODIFIED_DATE_INVALID
## 28 mdatunl MODIFIED_DATE_UNLIKELY
## 29 muldativ MULTIMEDIA_DATE_INVALID
## 30 muluriiv MULTIMEDIA_URI_INVALID
## 31 preneglat PRESUMED_NEGATED_LATITUDE
## 32 preneglon PRESUMED_NEGATED_LONGITUDE
## 33 preswcd PRESUMED_SWAPPED_COORDINATE
## 34 rdativ RECORDED_DATE_INVALID
## 35 rdatm RECORDED_DATE_MISMATCH
## 36 rdatunl RECORDED_DATE_UNLIKELY
## 37 refuriiv REFERENCES_URI_INVALID
## 38 txmatfuz TAXON_MATCH_FUZZY
## 39 txmathi TAXON_MATCH_HIGHERRANK
## 40 txmatnon TAXON_MATCH_NONE
## 41 typstativ TYPE_STATUS_INVALID
## 42 zerocd ZERO_COORDINATE
## 43 cdpi COORDINATE_PRECISION_INVALID
## 44 cdumi COORDINATE_UNCERTAINTY_METERS_INVALID
## 45 indci INDIVIDUAL_COUNT_INVALID
## 46 interr INTERPRETATION_ERROR
## 47 iccos INDIVIDUAL_COUNT_CONFLICTS_WITH_OCCURRENCE_STATUS
## 48 osiic OCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
## 49 osu OCCURRENCE_STATUS_UNPARSABLE
## 50 geodi GEOREFERENCED_DATE_INVALID
## 51 geodu GEOREFERENCED_DATE_UNLIKELY
## 52 ambcol AMBIGUOUS_COLLECTION
## 53 ambinst AMBIGUOUS_INSTITUTION
## 54 colmafu COLLECTION_MATCH_FUZZY
## 55 colmano COLLECTION_MATCH_NONE
## 56 incomis INSTITUTION_COLLECTION_MISMATCH
## 57 inmafu INSTITUTION_MATCH_FUZZY
## 58 inmano INSTITUTION_MATCH_NONE
## 59 osifbor OCCURRENCE_STATUS_INFERRED_FROM_BASIS_OF_RECORD
## 60 diffown DIFFERENT_OWNER_INSTITUTION
## 61 taxmatagg TAXON_MATCH_AGGREGATE
## 62 fpsrsinv FOOTPRINT_SRS_INVALID
## 63 fpwktinv FOOTPRINT_WKT_INVALID
## 64 anm ACCEPTED_NAME_MISSING
## 65 annu ACCEPTED_NAME_NOT_UNIQUE
## 66 anuidi ACCEPTED_NAME_USAGE_ID_INVALID
## 67 aitidinv ALT_IDENTIFIER_INVALID
## 68 bbmn BACKBONE_MATCH_NONE
## 69 basauthm BASIONYM_AUTHOR_MISMATCH
## 70 bibrinv BIB_REFERENCE_INVALID
## 71 chsun CHAINED_SYNOYM
## 72 clasna CLASSIFICATION_NOT_APPLIED
## 73 clasroi CLASSIFICATION_RANK_ORDER_INVALID
## 74 conbascomb CONFLICTING_BASIONYM_COMBINATION
## 75 desinv DESCRIPTION_INVALID
## 76 disinv DISTRIBUTION_INVALID
## 77 hom HOMONYM
## 78 minv MULTIMEDIA_INVALID
## 79 npm NAME_PARENT_MISMATCH
## 80 ns NO_SPECIES
## 81 nsinv NOMENCLATURAL_STATUS_INVALID
## 82 onder ORIGINAL_NAME_DERIVED
## 83 onnu ORIGINAL_NAME_NOT_UNIQUE
## 84 onuidinv ORIGINAL_NAME_USAGE_ID_INVALID
## 85 ov ORTHOGRAPHIC_VARIANT
## 86 pc PARENT_CYCLE
## 87 pnnu PARENT_NAME_NOT_UNIQUE
## 88 pnuidinv PARENT_NAME_USAGE_ID_INVALID
## 89 pp PARTIALLY_PARSABLE
## 90 pbg PUBLISHED_BEFORE_GENUS
## 91 rankinv RANK_INVALID
## 92 relmiss RELATIONSHIP_MISSING
## 93 scina SCIENTIFIC_NAME_ASSEMBLED
## 94 spprinv SPECIES_PROFILE_INVALID
## 95 taxstinv TAXONOMIC_STATUS_INVALID
## 96 taxstmis TAXONOMIC_STATUS_MISMATCH
## 97 unpars UNPARSABLE
## 98 vernnameinv VERNACULAR_NAME_INVALID
## 99 backmatagg BACKBONE_MATCH_AGGREGATE
## description
## 1 The given basis of record is impossible to interpret or seriously different from the recommended vocabulary.
## 2 The interpreted continent and country do not match up.
## 3 The interpreted continent is based on the coordinates, not the verbatim string information.
## 4 Uninterpretable continent values found.
## 5 Coordinate value given in some form but GBIF is unable to interpret it.
## 6 Coordinate has invalid lat/lon values out of their decimal max range.
## 7 The original coordinate was successfully reprojected from a different geodetic datum to WGS84.
## 8 The given decimal latitude and longitude could not be reprojected to WGS84 based on the provided datum.
## 9 Indicates successful coordinate reprojection according to provided datum, but which results in a datum shift larger than 0.1 decimal degrees.
## 10 Original coordinate modified by rounding to 5 decimals.
## 11 The interpreted occurrence coordinates fall outside of the indicated country.
## 12 The interpreted country is based on the coordinates, not the verbatim string information.
## 13 Uninterpretable country values found.
## 14 Interpreted country for dwc:country and dwc:countryCode contradict each other.
## 15 Set if supplied min>max
## 16 Set if depth is a non numeric value
## 17 Set if supplied depth is not given in the metric system, for example using feet instead of meters
## 18 Set if depth is larger than 11.000m or negative.
## 19 Set if supplied min > max elevation
## 20 Set if elevation is a non numeric value
## 21 Set if supplied elevation is not given in the metric system, for example using feet instead of meters
## 22 Set if elevation is above the troposphere (17km) or below 11km (Mariana Trench).
## 23 Indicating that the interpreted coordinates assume they are based on WGS84 datum as the datum was either not indicated or interpretable.
## 24 The geodetic datum given could not be interpreted.
## 25 The date given for dwc:dateIdentified is invalid and cant be interpreted at all.
## 26 The date given for dwc:dateIdentified is in the future or before Linnean times (1700).
## 27 A (partial) invalid date is given for dc:modified, such as a non existing date, invalid zero month, etc.
## 28 The date given for dc:modified is in the future or predates unix time (1970).
## 29 An invalid date is given for dc:created of a multimedia object.
## 30 An invalid uri is given for a multimedia object.
## 31 Latitude appears to be negated, e.g. 32.3 instead of -32.3
## 32 Longitude appears to be negated, e.g. 32.3 instead of -32.3
## 33 Latitude and longitude appear to be swapped.
## 34 A (partial) invalid date is given, such as a non existing date, invalid zero month, etc.
## 35 The recording date specified as the eventDate string and the individual year, month, day are contradicting.
## 36 The recording date is highly unlikely, falling either into the future or represents a very old date before 1600 that predates modern taxonomy.
## 37 An invalid uri is given for dc:references.
## 38 Matching to the taxonomic backbone can only be done using a fuzzy, non exact match.
## 39 Matching to the taxonomic backbone can only be done on a higher rank and not the scientific name.
## 40 Matching to the taxonomic backbone cannot be done cause there was no match at all or several matches with too little information to keep them apart (homonyms).
## 41 The given type status is impossible to interpret or seriously different from the recommended vocabulary.
## 42 Coordinate is the exact 0/0 coordinate, often indicating a bad null coordinate.
## 43 Indicates an invalid or very unlikely coordinatePrecision
## 44 Indicates an invalid or very unlikely dwc:uncertaintyInMeters.
## 45 Individual count value not parsable into an integer.
## 46 An error occurred during interpretation, leaving the record interpretation incomplete.
## 47 Example: individual count value > 0, but occurrence status is absent and etc.
## 48 Occurrence status was inferred from the individual count value
## 49 Occurrence status value can't be assigned to OccurrenceStatus
## 50 The date given for dwc:georeferencedDate is invalid and can't be interpreted at all.
## 51 The date given for dwc:georeferencedDate is in the future or before Linnean times (1700).
## 52 The given collection matches with more than 1 GrSciColl collection.
## 53 The given institution matches with more than 1 GrSciColl institution.
## 54 The given collection was fuzzily matched to a GrSciColl collection.
## 55 The given collection couldn't be matched with any GrSciColl collection.
## 56 The collection matched doesn't belong to the institution matched.
## 57 The given institution was fuzzily matched to a GrSciColl institution.
## 58 The given institution couldn't be matched with any GrSciColl institution.
## 59 Occurrence status was inferred from basis of records
## 60 The given owner institution is different than the given institution. Therefore we assume it doesn't belong to the institution and we don't link it to the occurrence.
## 61 Matching to the taxonomic backbone can only be done on a species level, but the occurrence was in fact considered a broader species aggregate/complex.
## 62 The Footprint Spatial Reference System given could not be interpreted
## 63 The Footprint Well-Known-Text given could not be interpreted
## 64 Synonym lacking an accepted name.
## 65 Synonym has a verbatim accepted name which is not unique and refers to several records.
## 66 The value for dwc:acceptedNameUsageID could not be resolved.
## 67 At least one alternative identifier extension record attached to this name usage is invalid.
## 68 Name usage could not be matched to the GBIF backbone.
## 69 The authorship of the original name does not match the authorship in brackets of the actual name.
## 70 At least one bibliographic reference extension record attached to this name usage is invalid.
## 71 If a synonym points to another synonym as its accepted taxon the chain is resolved.
## 72 The denormalized classification could not be applied to the name usage.
## 73 The given ranks of the names in the classification hierarchy do not follow the hierarchy of ranks.
## 74 There have been more than one accepted name in a homotypical basionym group of names.
## 75 At least one description extension record attached to this name usage is invalid.
## 76 At least one distribution extension record attached to this name usage is invalid.
## 77 A not synonymized homonym exists for this name in some other backbone source which have been ignored at build time.
## 78 At least one multimedia extension record attached to this name usage is invalid.
## 79 The (accepted) bi/trinomial name does not match the parent name and should be recombined into the parent genus/species.
## 80 The group (currently only genera are tested) are lacking any accepted species GBIF backbone specific issue.
## 81 dwc:nomenclaturalStatus could not be interpreted
## 82 Record has a original name (basionym) relationship which was derived from name & authorship comparison, but did not exist explicitly in the data.
## 83 Record has a verbatim original name (basionym) which is not unique and refers to several records.
## 84 The value for dwc:originalNameUsageID could not be resolved.
## 85 A potential orthographic variant exists in the backbone.
## 86 The child parent classification resulted into a cycle that needed to be resolved/cut.
## 87 Record has a verbatim parent name which is not unique and refers to several records.
## 88 The value for dwc:parentNameUsageID could not be resolved.
## 89 The beginning of the scientific name string was parsed, but there is additional information in the string that was not understood.
## 90 A bi/trinomial name published earlier than the parent genus was published.
## 91 dwc:taxonRank could not be interpreted
## 92 There were problems representing all name usage relationships, i.e.
## 93 The scientific name was assembled from the individual name parts and not given as a whole string.
## 94 At least one species profile extension record attached to this name usage is invalid.
## 95 dwc:taxonomicStatus could not be interpreted
## 96 no description
## 97 The scientific name string could not be parsed at all, but appears to be a parsable name type, i.e.
## 98 At least one vernacular name extension record attached to this name usage is invalid.
## 99 Name usage could only be matched to a GBIF backbone species, but was in fact a broader species aggregate/complex.
## type
## 1 occurrence
## 2 occurrence
## 3 occurrence
## 4 occurrence
## 5 occurrence
## 6 occurrence
## 7 occurrence
## 8 occurrence
## 9 occurrence
## 10 occurrence
## 11 occurrence
## 12 occurrence
## 13 occurrence
## 14 occurrence
## 15 occurrence
## 16 occurrence
## 17 occurrence
## 18 occurrence
## 19 occurrence
## 20 occurrence
## 21 occurrence
## 22 occurrence
## 23 occurrence
## 24 occurrence
## 25 occurrence
## 26 occurrence
## 27 occurrence
## 28 occurrence
## 29 occurrence
## 30 occurrence
## 31 occurrence
## 32 occurrence
## 33 occurrence
## 34 occurrence
## 35 occurrence
## 36 occurrence
## 37 occurrence
## 38 occurrence
## 39 occurrence
## 40 occurrence
## 41 occurrence
## 42 occurrence
## 43 occurrence
## 44 occurrence
## 45 occurrence
## 46 occurrence
## 47 occurrence
## 48 occurrence
## 49 occurrence
## 50 occurrence
## 51 occurrence
## 52 occurrence
## 53 occurrence
## 54 occurrence
## 55 occurrence
## 56 occurrence
## 57 occurrence
## 58 occurrence
## 59 occurrence
## 60 occurrence
## 61 occurrence
## 62 occurrence
## 63 occurrence
## 64 name
## 65 name
## 66 name
## 67 name
## 68 name
## 69 name
## 70 name
## 71 name
## 72 name
## 73 name
## 74 name
## 75 name
## 76 name
## 77 name
## 78 name
## 79 name
## 80 name
## 81 name
## 82 name
## 83 name
## 84 name
## 85 name
## 86 name
## 87 name
## 88 name
## 89 name
## 90 name
## 91 name
## 92 name
## 93 name
## 94 name
## 95 name
## 96 name
## 97 name
## 98 name
## 99 name#checagem dos problemas encontrados na base
flying_gbif$data$issues %>%
unique() %>%
strsplit(., "[,]") %>%
unlist()## [1] "cdround" "cdround" "cudc" "gass84" "cum" "gass84"
## [7] "fpwktinv" "gass84" "osiic" "diffown" "gass84" "gdativ"
## [13] "colmafu" "inmafu" "cdreps" "cdround" "inmano" "conti"
## [19] "gass84" "cdround" "osiic" "cdround" "depmms" "typstativ"
## [25] "osiic" "inmano" "cdround" "gass84" "cudc" "gass84"
## [31] "cdround" "depmms" "elmms" "typstativ" "osiic" "inmano"
## [37] "gass84" "gdativ" "refuriiv"O conjunto de dados retornado pelo GBIF apresenta diversas variáveis,
mas utilizaremos apenas 14 para começarmos a nossa análise. Assim,
selecionaremos por meio do pacote dplyr as variáveis de
interesse e geraremos um data frame com 500 observações, em que cada
observação corresponde a uma ocorrência da espécie. Agora, utilizaremos
a funçãodistinct para verificar as observações, o que
reduziu o nosso data frame para 461 observações. Então, utilizaremos a
função unique para remover as observações duplicadas, como
no script e no output abaixo:
#seleção de variáveis
flying_gbif1 <- flying_gbif$data %>%
dplyr::select(scientificName, acceptedScientificName, decimalLatitude, decimalLongitude,
issues, waterBody, basisOfRecord, occurrenceStatus, rightsHolder,
datasetName, recordedBy, depth, locality, habitat, year)
#ocorrências unicas
flying_gbif1 <- flying_gbif1 %>%
distinct()
# checar niveis dos fatores
lapply(flying_gbif1, unique)## $scientificName
## [1] "Dactylopterus volitans (Linnaeus, 1758)"
## [2] "BOLD:AAB9833"
##
## $acceptedScientificName
## [1] "Dactylopterus volitans (Linnaeus, 1758)"
## [2] "BOLD:AAB9833"
##
## $decimalLatitude
## [1] -22.938775 -22.994415 37.084909 18.472243 -23.014619 21.144459
## [7] 12.604672 39.360083 11.268465 39.448878 39.448931 14.029178
## [13] 39.088752 39.449634 42.368025 39.448435 39.447788 12.521525
## [19] 36.720843 41.421000 18.428530 40.052190 38.091298 -34.684100
## [25] 40.193427 -38.551050 38.731004 17.634067 17.633238 41.387356
## [31] 44.657141 17.620576 9.636069 34.617847 21.290365 41.916332
## [37] 12.133924 -22.990787 22.120637 19.351058 12.518484 42.365361
## [43] 43.680367 43.687389 40.408868 39.132195 12.521620 26.782984
## [49] 35.934040 42.538126 42.536637 42.536417 11.154870 36.996504
## [55] 45.528336 44.632480 43.318600 43.320000 -23.033885 20.485278
## [61] 17.891213 39.151353 9.255185 26.782915 43.781012 18.708028
## [67] 41.916813 43.229036 15.773260 40.377449 20.472701 39.080793
## [73] 38.624125 42.214137 19.271623 12.110460 12.143384 26.783231
## [79] 12.117240 26.783365 14.729990 12.121913 12.023785 -17.828611
## [85] -17.759444 -23.124924 11.219170 11.273350 41.916694 41.916774
## [91] 41.916642 39.536000 39.466940 12.318043 38.619478 39.202068
## [97] 11.207970 41.862614 26.783003 28.443300 20.484722 12.555902
## [103] 4.219720 12.566334 -23.109408 -23.169070 17.482080 -17.751944
## [109] 40.361210 18.341895 43.780304 17.914532 18.216815 14.756833
## [115] 14.655996 0.403352 0.403318 12.008061 18.218325 18.219410
## [121] 36.020300 15.793972 36.738936 13.067341 13.069592 -22.956500
## [127] -23.005735 14.443092 39.791512 15.554900 24.041212 -19.749167
## [133] 12.318116 12.021200 40.881574 47.522683 12.536642 16.234670
## [139] 10.309271 24.030160 20.809369 16.098915 -23.971500 39.529000
## [145] -13.008600 14.785620 36.018898 36.018153 38.500000 38.510000
## [151] 38.620000 39.486000 38.440000 41.917222 36.019900 39.833052
## [157] 31.518349 38.560000 15.277094 9.499519 35.204700 24.068598
## [163] 12.467493 -23.148705 16.308940 41.570700 20.866853 10.220227
## [169] 24.661640 24.661470 24.722690 24.722610 24.723970 24.723890
## [175] 24.578580 24.578540 24.582760 24.582630 24.584000 24.583940
## [181] 24.591300 24.718970 24.719050 24.591220 24.723940 24.722510
## [187] 24.722460 24.724440 24.722470 24.722540 24.724310 24.731380
## [193] 24.724370 24.724070 24.732960 24.724260 24.732870 24.594170
## [199] 24.597540 24.597590 24.594210 24.598170 24.598120 24.712570
## [205] 24.618170 24.712490 24.626950 24.626910 24.634810 24.634750
## [211] 24.719440 24.623900 24.720970 24.623970 24.721170 24.722440
## [217] 24.722450 24.573360 24.573420 24.594670 24.594780 24.592850
## [223] 24.592810 24.595440 24.587400 24.593090 24.598590 24.589100
## [229] 24.589130 24.595550 24.587450 24.678200 24.667080 24.666920
## [235] 24.661570 24.678280 24.661220 24.581490 24.581520 24.581100
## [241] 24.592630 24.592550 24.581030 24.723150 24.671770 24.675300
## [247] 24.675260 24.712840 24.712940 24.570740 24.570700 24.629140
## [253] 24.629170 24.658610 24.658620 24.660400 24.660540 24.666410
## [259] 24.666250 24.669310 24.669070 24.670300 24.670440 24.690700
## [265] 24.690880 24.715390 24.715450 24.624960 24.625060 24.628800
## [271] 24.628720 24.630980 24.705400 24.705470 24.712410 24.712510
## [277] 24.720680 24.720660 24.726650 24.726690 24.726240 24.726340
## [283] 24.728450 24.728420 24.726830 24.729810 24.729870 24.634340
## [289] 24.637600 24.634450 24.662500 24.662540 24.663940 24.664000
## [295] 24.688820 24.683420 24.683300 24.688760 24.626220 24.633200
## [301] 24.633290 24.626150 24.639760 24.639840 24.644540 24.626640
## [307] 24.626690 24.644550 24.680100 24.718360 24.688360 24.680130
## [313] 24.688370 24.718470 24.613550 24.641820 24.613470 24.641710
## [319] 24.611070 24.610980 24.715680 24.715730 24.717880 24.717810
## [325] 24.602050 24.602060 24.623240 24.623180 24.632980 24.632840
## [331] 24.640620 24.640760 24.686800 24.686910 24.641870 24.642780
## [337] 24.642750 24.643400 24.643490 24.618770 24.709050 24.709010
## [343] 24.618660 24.629600 24.629660 24.635120 24.635170 24.640250
## [349] 24.640310 24.648390 24.648450 24.645620 24.645500 24.616960
## [355] 24.615700 24.615640 24.616910 24.620240 24.620250 24.614300
## [361] 24.614500 24.655710 24.655750 24.618270 24.618400 24.632160
## [367] 24.632360 24.634960 24.636300 24.646890 24.646780 24.679500
## [373] 24.679680 24.707350 24.707420 24.677440 24.677420 24.681640
## [379] 24.681580 24.681970 24.682920 24.682040 24.682810 24.682390
## [385] 24.682350 24.686110 24.715900 24.715990 24.680310 24.680210
## [391] 24.681030 24.681100 24.713740 24.719590 24.713700 24.719490
## [397] 24.694820 24.694880 24.721320 24.721030 24.685960 24.699120
## [403] 24.652580 24.661020 24.684940 24.700840 24.723200 24.733070
## [409] 24.643610 24.629000 24.642850 24.638950 24.711350 24.711770
## [415] 24.653780 24.653840 24.622320 24.713300 24.651740 24.684950
## [421] 24.697700 24.668950 24.663050 24.663220 24.720710 24.651110
## [427] 24.686030 24.708100 24.661130 24.718390 24.587540 24.616750
## [433] 24.677060 24.638870 24.711190 24.683890 24.645440
##
## $decimalLongitude
## [1] -42.288895 -43.176417 6.462393 -67.168947 -44.279129 -86.793512
## [7] -70.052447 3.225275 -74.195602 2.742134 2.742160 -16.771705
## [13] 23.698859 2.740545 10.877323 2.741765 2.742548 -81.741386
## [19] -3.728723 9.106261 -77.153114 4.053591 20.569633 -54.278187
## [25] 23.684955 -58.565856 1.392308 -63.263442 -63.259275 12.909748
## [31] -63.944251 -63.264091 -82.660371 33.005539 -89.666009 3.209454
## [37] -68.985271 -43.165480 -81.119334 -81.270147 -81.729409 18.662894
## [43] 7.237969 7.246095 17.209440 9.472741 -69.968338 -80.044254
## [49] 14.344284 3.059085 3.058447 3.058573 -74.227787 7.183845
## [55] -60.973655 -63.934948 -8.763800 -8.760000 -43.202450 -86.972449
## [61] -62.848508 23.323827 -82.142522 -80.044685 7.648342 -87.707885
## [67] 3.208984 5.347821 -87.465350 17.297248 -86.993588 17.127110
## [73] 20.604304 3.125282 -81.313130 -68.983968 -69.026961 -80.044444
## [79] -68.966977 -80.040597 -61.180650 -68.969679 -61.764437 -39.200278
## [85] -39.181667 -44.278558 -74.240580 -74.206970 3.208550 3.208636
## [91] 3.208708 2.376600 26.208938 -69.151315 15.826300 23.338677
## [97] -74.239700 3.166441 -80.044070 -84.516100 -86.951005 -81.716394
## [103] 118.695000 -81.690855 -44.230542 -44.144081 -62.990555 -39.181389
## [109] 17.312072 -64.978137 7.652152 -71.672745 -70.562675 -60.965848
## [115] -61.154659 6.690227 6.690225 -61.794923 -70.560572 -70.564241
## [121] 14.271400 -79.848611 27.994558 -59.602013 -59.584265 -42.833389
## [127] -44.315759 -60.881495 2.692984 -61.464900 -74.527473 -39.697500
## [133] -69.151289 -61.799900 -72.495531 -61.577783 -81.718098 -61.353560
## [139] -75.582339 -74.528160 -86.848397 -86.896032 -46.334801 2.439300
## [145] -38.533800 -61.217640 14.272312 14.272635 -0.120000 0.030000
## [151] 0.010000 0.040000 2.476800 -0.330000 3.207778 14.271800
## [157] 3.122341 34.428866 -0.040000 -23.766157 -78.726826 25.722100
## [163] -74.533155 -61.499668 -44.349789 -61.798440 2.541830 -86.867317
## [169] -75.616706 -82.847520 -82.847430 -82.831270 -82.831200 -82.813690
## [175] -82.813520 -82.918530 -82.918400 -82.911790 -82.911770 -82.903100
## [181] -82.902950 -82.876090 -82.774430 -82.774380 -82.876110 -82.783870
## [187] -82.796570 -82.796740 -82.806990 -82.784900 -82.785130 -82.784210
## [193] -82.796010 -82.774020 -82.790980 -82.774540 -82.795970 -82.790760
## [199] -82.795690 -82.912680 -82.869950 -82.869970 -82.912480 -82.915920
## [205] -82.915720 -82.794320 -82.865700 -82.865560 -82.794170 -82.855350
## [211] -82.855450 -82.858030 -82.857990 -82.795840 -82.859710 -82.835100
## [217] -82.859740 -82.835050 -82.788390 -82.788310 -82.911040 -82.911200
## [223] -82.878070 -82.877920 -82.885320 -82.885200 -82.873020 -82.899200
## [229] -82.909330 -82.916060 -82.894320 -82.894200 -82.899110 -82.823430
## [235] -82.834480 -82.834520 -82.845050 -82.823000 -82.844900 -82.907820
## [241] -82.907930 -82.888020 -82.871990 -82.871940 -82.887880 -82.774870
## [247] -82.817310 -82.817360 -82.826040 -82.825990 -82.791960 -82.792860
## [253] -82.906360 -82.906250 -82.965510 -82.965620 -82.938050 -82.938130
## [259] -82.937480 -82.937570 -82.929720 -82.929780 -82.928050 -82.927870
## [265] -82.927530 -82.927660 -82.886050 -82.885860 -82.897410 -82.897360
## [271] -82.900570 -82.900510 -82.897120 -82.897170 -82.968200 -82.872070
## [277] -82.872130 -82.888000 -82.888130 -82.854920 -82.855000 -82.810020
## [283] -82.810100 -82.805160 -82.805400 -82.804280 -82.804490 -82.812630
## [289] -82.804400 -82.801030 -82.801020 -82.961240 -82.957590 -82.961250
## [295] -82.852110 -82.851980 -82.932910 -82.932970 -82.886230 -82.897290
## [301] -82.897180 -82.886340 -82.854140 -82.854530 -82.854580 -82.854060
## [307] -82.961740 -82.961630 -82.928960 -82.899270 -82.899180 -82.929160
## [313] -82.904600 -82.849830 -82.884100 -82.884040 -82.849910 -82.902300
## [319] -82.929760 -82.902340 -82.929750 -82.902540 -82.902530 -82.865840
## [325] -82.865680 -82.859320 -82.859090 -82.910730 -82.910830 -82.901760
## [331] -82.901580 -82.966800 -82.966880 -82.966980 -82.967010 -82.907520
## [337] -82.907490 -82.965200 -82.965300 -82.969270 -82.969380 -82.966050
## [343] -83.077900 -82.906320 -82.906210 -83.077850 -83.101550 -83.101370
## [349] -83.101950 -83.101960 -83.103010 -83.103080 -83.031310 -83.031220
## [355] -83.077600 -83.077990 -83.079000 -83.077490 -83.077620 -83.079090
## [361] -83.084080 -83.084310 -83.066360 -83.066450 -83.085220 -83.085210
## [367] -83.062910 -83.062850 -83.095730 -83.095740 -83.066100 -83.060090
## [373] -83.065890 -83.060050 -83.031230 -83.030900 -83.030930 -82.985800
## [379] -82.985920 -83.017710 -83.017550 -83.031840 -83.031930 -83.032530
## [385] -83.043230 -83.032580 -83.043430 -83.011990 -83.012060 -83.050770
## [391] -83.050650 -82.982190 -82.982200 -83.017530 -83.034020 -83.033720
## [397] -82.994270 -82.984620 -82.994370 -82.984540 -83.005380 -83.005460
## [403] -82.983490 -82.983320 -82.985050 -83.013470 -83.018010 -83.011050
## [409] -83.010950 -82.990150 -82.996780 -83.005390 -82.827840 -82.791600
## [415] -82.858390 -82.930790 -82.932120 -82.883690 -82.901850 -83.101510
## [421] -83.101430 -83.060020 -82.993290 -83.007560 -83.032600 -83.014460
## [427] -83.093110 -83.095850 -83.095750 -82.985680 -83.002410 -83.013540
## [433] -82.984820 -82.990080 -82.996850 -82.841850 -82.907350 -82.866620
## [439] -82.826650 -82.932050 -82.883880 -82.906270 -83.074930
##
## $issues
## [1] ""
## [2] "cdround"
## [3] "cdround,cudc"
## [4] "gass84"
## [5] "cum,gass84,fpwktinv"
## [6] "gass84,osiic,diffown"
## [7] "gass84,gdativ,colmafu,inmafu"
## [8] "cdreps"
## [9] "cdround,inmano"
## [10] "conti,gass84"
## [11] "cdround,osiic"
## [12] "cdround,depmms,typstativ,osiic,inmano"
## [13] "cdround,gass84"
## [14] "cudc,gass84"
## [15] "cdround,depmms,elmms,typstativ,osiic,inmano"
## [16] "gass84,gdativ,refuriiv"
##
## $waterBody
## [1] NA
## [2] "Mar Caribe"
## [3] "Gulf of Mexico"
## [4] "Celebes Sea"
## [5] "Mer Méditerranée"
## [6] "Atlantic, Caribbean Sea, Prince Rupert Bay"
## [7] "Caribbean"
## [8] "South Atlantic Ocean"
## [9] "Mediterranean Sea"
## [10] "Mirabello bay"
## [11] "Mediterranean"
##
## $basisOfRecord
## [1] "HUMAN_OBSERVATION" "MATERIAL_SAMPLE" "PRESERVED_SPECIMEN"
## [4] "MACHINE_OBSERVATION"
##
## $occurrenceStatus
## [1] "PRESENT" "ABSENT"
##
## $rightsHolder
## [1] "John Christopher"
## [2] "Eric Fischer Rempe"
## [3] "Karim Haddad"
## [4] "dtsapalas"
## [5] "claudia_mermelstein"
## [6] "Andres Garcia"
## [7] "Caroline Synakowski"
## [8] "Luis Pérez Berrocal"
## [9] "Julian Alzate"
## [10] "Lucy Keith-Diagne"
## [11] "marieta55"
## [12] "Valentin Moser"
## [13] "Ana Castrillon"
## [14] "ictio"
## [15] "Julien Renoult"
## [16] "Sheldon Logan"
## [17] "Frédéric ANDRE"
## [18] "Falk Viczian Solarboot-Projekte gGmbH"
## [19] "Martin Coronel Varela"
## [20] "George Manavopoulos"
## [21] "Edgar Romeo"
## [22] "ocean_explorers"
## [23] "terence zahner"
## [24] "Dino Biancolini"
## [25] "daisymagoo"
## [26] "Julius Stölzle"
## [27] "marinakyprianou"
## [28] "Jay"
## [29] "josepvilanova"
## [30] "Paweł Pieluszyński"
## [31] "James Telford"
## [32] "portiadog"
## [33] "xaviervilport"
## [34] "Павел Несмеянов"
## [35] "tmenut"
## [36] "Giulia P"
## [37] "Paola Casale"
## [38] "oversteegen"
## [39] "Pauline Walsh Jacobson"
## [40] "maltamama"
## [41] "Xavier Rufray"
## [42] "Sylvain Le Bris"
## [43] "forajido"
## [44] "chris2184"
## [45] "hunterefs"
## [46] NA
## [47] "Austen Novis"
## [48] "Carlo R Sanchez"
## [49] "Karl Questel"
## [50] "Chris Taklis"
## [51] "stacebird"
## [52] "Chiaramonte family"
## [53] "Idlegrraphics"
## [54] "George Allen"
## [55] "Neil DeMaster"
## [56] "frahome"
## [57] "Papageorgiou Nikolaos"
## [58] "Bernat Garrigós"
## [59] "Rachel Andres-Beck"
## [60] "apedretti"
## [61] "mbrunetto"
## [62] "sandranap"
## [63] "Werner de Gier"
## [64] "sflott"
## [65] "MBML-Peixes - Coleção de Peixes"
## [66] "renata lepage"
## [67] "MoAm SAS | CORPAMAG"
## [68] "Merav Vonshak"
## [69] "vanilendil"
## [70] "silvio_lioce1"
## [71] "mothpup"
## [72] "Rahul Joshi"
## [73] "Diveboard"
## [74] "Alejandro"
## [75] "Dicabu"
## [76] "Steve Kastner"
## [77] "gianfrs"
## [78] "John C."
## [79] "Patricia Torres Pineda"
## [80] "Francesco Cecere"
## [81] "jbelback"
## [82] "Ana Carolina Hernández-Oquet"
## [83] "INVEMAR"
## [84] "Alphons"
## [85] "Curtis Irving"
## [86] "João D'Andretta"
## [87] "Erika Mitchell"
## [88] "Antoni López-Arenas i Cama"
## [89] "maycl"
## [90] "Stichting Observation International"
## [91] "agardner"
## [92] "Claude Nozères"
## [93] "Carlos Gonzalez"
## [94] "Greg Lawrence"
## [95] "Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO)"
## [96] "bronyaur"
## [97] "heliastes21"
## [98] "Anita Sprungk"
## [99] "Mika Tomta"
## [100] "Carmelo López Abad"
## [101] "bensonk"
## [102] "Alex Ward"
## [103] "José Valério"
## [104] "gamesman02"
##
## $datasetName
## [1] "iNaturalist research-grade observations"
## [2] NA
## [3] "MBML-Peixes - Coleção de Peixes"
## [4] "CORPAMAG-MOAM-CONTRATO260-2017-ARRECIFES-PASTOS-2018"
## [5] "Global Marine biodiversity data from Seawatchers Marine Citizen Science Platform 1980-2020"
## [6] "Diveboard - Scuba diving citizen science"
## [7] "Biodiversidad íctica de la Isla Cayo Serranilla Expedición Seaflower 2017 Proyecto Colombia BIO"
## [8] "Base de dados da Coleção Ictiológica do LEP-UFRRJ"
## [9] "NMNH Extant Biology"
## [10] "NMNH Material Samples (USNM)"
## [11] "Inventario ictiofaunístico de los humedales de Puerto Morelos, Quintana Roo"
## [12] "Colección de referencia de otolitos, Instituto de Ciencias del Mar-CSIC"
## [13] "Dry Tortugas Reef Visual Census 2014"
##
## $recordedBy
## [1] "John Christopher"
## [2] "Eric Fischer Rempe"
## [3] "Karim Haddad"
## [4] "dtsapalas"
## [5] "claudia_mermelstein"
## [6] "Andres Garcia"
## [7] "Caroline Synakowski"
## [8] "Luis Pérez Berrocal"
## [9] "Julian Alzate"
## [10] "Lucy Keith-Diagne"
## [11] "marieta55"
## [12] "Valentin Moser"
## [13] "Ana Castrillon"
## [14] "ictio"
## [15] "Julien Renoult"
## [16] "Sheldon Logan"
## [17] "Frédéric ANDRE"
## [18] "Falk Viczian Solarboot-Projekte gGmbH"
## [19] "Martin Coronel Varela"
## [20] "George Manavopoulos"
## [21] "Edgar Romeo"
## [22] "ocean_explorers"
## [23] "terence zahner"
## [24] "Dino Biancolini"
## [25] "daisymagoo"
## [26] "Julius Stölzle"
## [27] "marinakyprianou"
## [28] "Jay"
## [29] "josepvilanova"
## [30] "Paweł Pieluszyński"
## [31] "James Telford"
## [32] "portiadog"
## [33] "xaviervilport"
## [34] "Павел Несмеянов"
## [35] "tmenut"
## [36] "Giulia P"
## [37] "Paola Casale"
## [38] "oversteegen"
## [39] "Pauline Walsh Jacobson"
## [40] "maltamama"
## [41] "Xavier Rufray"
## [42] "Sylvain Le Bris"
## [43] "forajido"
## [44] "chris2184"
## [45] "hunterefs"
## [46] "Rafael Banon"
## [47] "Austen Novis"
## [48] "Carlo R Sanchez"
## [49] "Karl Questel"
## [50] "Chris Taklis"
## [51] "stacebird"
## [52] "Chiaramonte family"
## [53] "Idlegrraphics"
## [54] "George Allen"
## [55] "Neil DeMaster"
## [56] "frahome"
## [57] "Papageorgiou Nikolaos"
## [58] "Bernat Garrigós"
## [59] "Rachel Andres-Beck"
## [60] "apedretti"
## [61] "mbrunetto"
## [62] "sandranap"
## [63] "Benjamin Guichard (BioObs)"
## [64] "Werner de Gier"
## [65] "sflott"
## [66] "Orlando Bastião Surlogalli"
## [67] "renata lepage"
## [68] NA
## [69] "Merav Vonshak"
## [70] "vanilendil"
## [71] "silvio_lioce1"
## [72] "R/V Oregon II, NOAA"
## [73] "mothpup"
## [74] "Rahul Joshi"
## [75] "Thomas Chardon"
## [76] "Alejandro"
## [77] "Dicabu"
## [78] "Steve Kastner"
## [79] "gianfrs"
## [80] "John C."
## [81] "Patricia Torres Pineda"
## [82] "Francesco Cecere"
## [83] "jbelback"
## [84] "Ana Carolina Hernández-Oquet"
## [85] "Anonymous"
## [86] "Arturo Acero-Pizarro|Andrea Polanco-Fernández|José-Julian Tavera|Nacor Bolaños-Cubillos"
## [87] "Alphons"
## [88] "Curtis Irving"
## [89] "T. P. Franco"
## [90] "João D'Andretta"
## [91] "Erika Mitchell"
## [92] "Antoni López-Arenas i Cama"
## [93] "L. Weigt & T. Christiaan"
## [94] "maycl"
## [95] "Lauana S. Fadini"
## [96] "Jacksonian"
## [97] "agardner"
## [98] "Claude Nozères"
## [99] "Carlos Gonzalez"
## [100] "Adrien Weckel (BioObs)"
## [101] "Greg Lawrence"
## [102] "ALMV; FMS; YLA; ODD"
## [103] "bronyaur"
## [104] "heliastes21"
## [105] "Maguelone GRATEAU, Henri GRATEAU (Bio CODEP 26-07)"
## [106] "jean-pierre, jean, corine (bio 26-07)"
## [107] "-927652254"
## [108] "Anita Sprungk"
## [109] "Institut Ecologia Litoral (IEL)"
## [110] "Mika Tomta"
## [111] "Carmelo López Abad"
## [112] "Pelagos"
## [113] "bensonk"
## [114] "Alex Ward"
## [115] "José Valério"
## [116] "Pascal RUEL (Aqua club Cruas)"
## [117] "Alfredo García de Vinuesa, Pilar Sánchez"
## [118] "MNLZ; ALMV; LFM; FMS; DZE"
## [119] "gamesman02"
##
## $depth
## [1] NA 46.000 15.000 28.000 10.565 6.710 0.000 21.500 40.000 10.000
## [11] 3.000 6.555 6.000 1.000 11.800 11.600 19.200 18.800 17.700 17.000
## [21] 11.000 10.900 10.700 11.200 10.100 9.000 14.000 24.700 25.000 14.200
## [31] 19.700 13.600 16.000 15.100 16.200 19.400 17.400 27.600 14.900 27.000
## [41] 17.900 13.900 18.500 13.300 14.800 16.600 15.300 12.700 12.200 7.800
## [51] 11.900 7.900 12.100 6.300 6.900 5.800 6.700 6.100 8.100 11.500
## [61] 8.500 5.100 6.400 9.800 10.600 9.400 16.300 24.900 13.700 13.000
## [71] 16.800 16.500 20.600 23.500 15.900 22.500 21.700 14.100 25.500 25.900
## [81] 8.700 23.700 18.600 9.500 25.800 14.300 16.700 17.300 17.600 16.900
## [91] 18.200 19.100 19.500 21.800 12.400 23.100 5.500 5.700 20.000 15.700
## [101] 15.200 19.800 20.100 8.400 6.600 13.100 10.200 9.200 12.800 13.400
## [111] 21.400 22.900 23.200 21.300 22.100 23.400 20.400 18.000 17.100 18.300
## [121] 22.300 25.600 26.400 14.500 16.400 21.600 20.900 24.600 23.800 22.400
## [131] 22.700 21.200 22.800 15.400 18.100 28.200 26.200 21.000 10.300 7.200
## [141] 7.000 24.000 14.600 15.500 29.200 26.500 10.400
##
## $locality
## [1] NA
## [2] "Galician waters, FAO 27"
## [3] " Galician waters, FAO 27"
## [4] "Próximo a praia de Barra Nova."
## [5] "Próximo ao Rio Caravelas."
## [6] "Bahía de Gaira"
## [7] "Bahía de Taganga"
## [8] "Gulf of Mexico"
## [9] "North Kampala Siusiu"
## [10] "Próximo a Rio Caravelas."
## [11] "Mgarr Ix Xini"
## [12] "Isla Cayo Serranilla"
## [13] "lagoa de Maricá"
## [14] "Prince Rupert Bay, Portsmouth, Dominica"
## [15] "Regência."
## [16] "Curaçao"
## [17] "Bianca C"
## [18] "Límite punta Sur del Parque"
## [19] "Maraldi"
## [20] "UTM25_33S_0425_3975"
## [21] "Illot de Benidorm"
## [22] "El Mascarat"
## [23] "Llomes de Reixes"
## [24] "Palestine - Gaza"
## [25] "El Albir"
## [26] "Nikolos Reef"
## [27] "Arenys de Mar"
## [28] "Frente a la CONANP"
##
## $habitat
## [1] NA "Formaciones Coralinas"
## [3] "Marine" "Pelágico"
## [5] "Arenal" "Continuous Low Relief"
## [7] "Continuous Medium Relief" "Spur and Groove High Relief"
## [9] "Isolated Low Relief (Patch)" "Isolated Medium Relief (Patch)"
## [11] "Isolated HR Relief (Patch)" "Spur and Groove low Relief"
## [13] "Continuous High Relief" "Rubble Low Relief"
##
## $year
## [1] 2022 2021 2020 2019 2018 2017 2016 2015 2014A checagem dos dados pode ser ainda mais refinada. Existem várias
possibilidades, mas a que exploraremos aqui é verificar se as
coordenadas disponibilizadas são realmente válidas, pois muitas vezes
essas coordenadas podem ser associadas a capitais, ou outros locais
terra firme, mas a espécie que estamos a analisar é marinha! Para isso
utilizamos os pacotes CoordinateCleaner e bcd,
como você pode visualizar abaixo:
library(bdc)
library(CoordinateCleaner)# checar coordenadas válidas
check_pf <-
bdc::bdc_coordinates_outOfRange(
data = flying_gbif1,
lat = "decimalLatitude",
lon = "decimalLongitude")##
## bdc_coordinates_outOfRange:
## Flagged 0 records.
## One column was added to the database.# checar coordenadas válidas e próximas a capitais (muitas vezes as coordenadas são erroneamente associadas a capitais dos países)
cl <- flying_gbif1 %>%
select(acceptedScientificName, decimalLatitude, decimalLongitude) %>%
rename(decimallongitude = decimalLongitude,
decimallatitude = decimalLatitude,
scientificName = acceptedScientificName) %>%
as_tibble() %>%
mutate(val = cc_val(., value = "flagged"),
sea = cc_sea(., value = "flagged"),
capital = cc_cap(., value = "flagged"))## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\letic\AppData\Local\Temp\RtmpEdLiZ8", layer: "ne_110m_land"
## with 127 features
## It has 3 fieldscl %>%
rename(decimalLongitude = decimallongitude,
decimalLatitude = decimallatitude) %>%
bdc::bdc_quickmap(., col_to_map = "capital") 
cl %>%
rename(decimalLongitude = decimallongitude,
decimalLatitude = decimallatitude) %>%
bdc::bdc_quickmap(., col_to_map = "sea") 
Os output que os pacote CoordinateCleaner e
bcd retornaram indicam que as coordenadas são válidas, mas
algumas ocorrências estão sinalizadas como próximas a capitas, essas não
nos interessam. Assim, para excluírmos essas ocorrências que não nos
interessam, em capitais e em terra firme, checaremos a distribuição do
peixe-voador nas regiões oceonográficas ao filtramos a variável
waterBody, variável essa que categoriza a região
oceográfica das de distribuição das espécies. Ainda, podemos visualizar
a distribuição das ocorrências do peixe-voador clivada por região com o
pacote ggplot, um pacote que nos permite visualizar
graficamente dados, como no comando, no output e no gráfico
abaixo.
# investigar niveis suspeitos
flying_gbif1 %>%
distinct(waterBody) %>%
pull()## [1] NA
## [2] "Mar Caribe"
## [3] "Gulf of Mexico"
## [4] "Celebes Sea"
## [5] "Mer Méditerranée"
## [6] "Atlantic, Caribbean Sea, Prince Rupert Bay"
## [7] "Caribbean"
## [8] "South Atlantic Ocean"
## [9] "Mediterranean Sea"
## [10] "Mirabello bay"
## [11] "Mediterranean"# waterBody
flying_gbif1 %>%
group_by(waterBody) %>%
summarise(occ = length(scientificName)) %>%
ggplot(aes(occ, y=waterBody)) +
geom_bar(stat = 'identity')
As ocorrências do peixe-voador se dão no Atlântico e no Mediterrâneo, logo, as ocorrências registradas no Mar de Celebes são suspeitas, pois Mar de Celebes localiza-se no Oceâno Pacífico. Então, vamos excluir essas ocorrências, mas manteremos as ocorrências categorizadas com NA nessa primeira análise.
# fonte das regioes erradas
flying_gbif1 %>%
filter(waterBody %in% c("Celebes Sea")) %>%
distinct(datasetName)## # A tibble: 1 × 1
## datasetName
## <chr>
## 1 Diveboard - Scuba diving citizen scienceA tabela que nós geramos após a filtragem apresentou que as ocorrências registradas por mergulhadores que são de uma plantaforma específica de ciência cidadã. A maioria das ocorrências suspeitas estavam associadas a esse tipo de coleta, por isso excluiremos as ocorrências associdadas a ela.
# 6 ocorrencias
flying_gbif1 %>%
filter(datasetName %in% c("Diveboard - Scuba diving citizen science"))## # A tibble: 6 × 15
## scientificName acceptedScienti… decimalLatitude decimalLongitude issues
## <chr> <chr> <dbl> <dbl> <chr>
## 1 Dactylopterus volita… Dactylopterus v… 4.22 119. cdreps
## 2 Dactylopterus volita… Dactylopterus v… 36.0 14.3 cdreps
## 3 Dactylopterus volita… Dactylopterus v… 12.0 -61.8 cdreps
## 4 Dactylopterus volita… Dactylopterus v… -13.0 -38.5 cdreps
## 5 Dactylopterus volita… Dactylopterus v… 36.0 14.3 cdreps
## 6 Dactylopterus volita… Dactylopterus v… 35.2 25.7 cdreps
## # … with 10 more variables: waterBody <chr>, basisOfRecord <chr>,
## # occurrenceStatus <chr>, rightsHolder <chr>, datasetName <chr>,
## # recordedBy <chr>, depth <dbl>, locality <chr>, habitat <chr>, year <int># filtrar todas do dataset suspeito
flying_gbif_ok <- flying_gbif1 %>%
filter(!datasetName %in% c("Diveboard - Scuba diving citizen science"))library(ggmap)
library(maps)
library(mapdata)
world <- map_data('world')# checar pontos
# sem ocorrências no Mar de Celebes!
ggplot() +
geom_polygon(data = world, aes(x = long, y = lat, group = group)) +
coord_fixed() +
theme_classic() +
geom_point(data = flying_gbif_ok, aes(x = decimalLongitude, y = decimalLatitude), color = "red") +
labs(x = "longitude", y = "latitude", title = expression(italic("Dactylopterus volitans")))
Outro critério para filtragem que vamos utilizar aqui é o da profundidade por região oceanográfica, pois o peixe-voador é encontrado em profundidades de no máximo 100 metros, como recifes, regiões estuarianas e em áreas arenosas.
# checar profundidade
flying_gbif_ok %>%
ggplot(aes(x = depth, fill = waterBody)) +
geom_histogram()
OBIS
Mas não só de GBIF vivem os pesquisadores! Nós podemos fazer essa
mesma limpeza de dados por meio OBIS, o outro repositório supracitado.
Aqui utlizaremos o pacote robis e a função
occurrence, que é similar ao occ_data do
rgbif. Além disso, podemos idenficar os problemas por meio
da coluna flag, como abaixo:
## OBIS
flying_obis <- robis::occurrence("Dactylopterus volitans")
# checar dados
names(flying_obis)## [1] "rightsHolder" "infraphylum"
## [3] "country" "date_year"
## [5] "scientificNameID" "scientificName"
## [7] "individualCount" "dropped"
## [9] "gigaclassid" "aphiaID"
## [11] "decimalLatitude" "subclassid"
## [13] "type" "gigaclass"
## [15] "infraphylumid" "phylumid"
## [17] "familyid" "catalogNumber"
## [19] "occurrenceStatus" "basisOfRecord"
## [21] "terrestrial" "id"
## [23] "parvphylum" "order"
## [25] "recordNumber" "dataset_id"
## [27] "locality" "decimalLongitude"
## [29] "collectionCode" "date_end"
## [31] "speciesid" "occurrenceID"
## [33] "license" "date_start"
## [35] "genus" "collectionID"
## [37] "eventDate" "brackish"
## [39] "coordinateUncertaintyInMeters" "absence"
## [41] "genusid" "originalScientificName"
## [43] "marine" "subphylumid"
## [45] "institutionCode" "wrims"
## [47] "date_mid" "class"
## [49] "orderid" "kingdom"
## [51] "classid" "phylum"
## [53] "species" "subphylum"
## [55] "subclass" "family"
## [57] "kingdomid" "parvphylumid"
## [59] "node_id" "flags"
## [61] "sss" "shoredistance"
## [63] "sst" "bathymetry"
## [65] "scientificNameAuthorship" "fieldNumber"
## [67] "waterBody" "occurrenceRemarks"
## [69] "year" "day"
## [71] "month" "recordedBy"
## [73] "institutionID" "language"
## [75] "modified" "datasetName"
## [77] "specificEpithet" "coordinatePrecision"
## [79] "datasetID" "dynamicProperties"
## [81] "county" "maximumDepthInMeters"
## [83] "bibliographicCitation" "continent"
## [85] "samplingEffort" "minimumDepthInMeters"
## [87] "geodeticDatum" "depth"
## [89] "stateProvince" "preparations"
## [91] "footprintSRS" "samplingProtocol"
## [93] "parentEventID" "eventID"
## [95] "eventRemarks" "footprintWKT"
## [97] "associatedReferences" "identifiedBy"
## [99] "sampleSizeUnit" "island"
## [101] "organismQuantityType" "taxonRank"
## [103] "countryCode" "georeferenceProtocol"
## [105] "verbatimCoordinateSystem" "sampleSizeValue"
## [107] "georeferenceRemarks" "minimumElevationInMeters"
## [109] "maximumElevationInMeters" "eventTime"
## [111] "sex" "references"
## [113] "higherGeography" "verbatimEventDate"
## [115] "higherClassification" "vernacularName"
## [117] "dataGeneralizations" "locationID"
## [119] "georeferenceSources" "verbatimDepth"
## [121] "islandGroup" "taxonRemarks"
## [123] "habitat" "fieldNotes"
## [125] "verbatimLocality" "dateIdentified"
## [127] "ownerInstitutionCode" "informationWithheld"
## [129] "accessRights" "identificationQualifier"
## [131] "taxonomicStatus" "locationRemarks"
## [133] "startDayOfYear" "locationAccordingTo"
## [135] "organismQuantity" "georeferencedDate"
## [137] "georeferencedBy" "georeferenceVerificationStatus"
## [139] "acceptedNameUsage" "lifeStage"
## [141] "verbatimLatitude" "higherGeographyID"
## [143] "verbatimLongitude" "nomenclaturalCode"
## [145] "identificationVerificationStatus" "identificationRemarks"
## [147] "verbatimSRS" "otherCatalogNumbers"
## [149] "associatedMedia" "associatedSequences"# check NA em datasetName
flying_obis1 <- flying_obis %>%
dplyr::select(scientificName, decimalLatitude, decimalLongitude, bathymetry,
flags, waterBody, basisOfRecord, occurrenceStatus, rightsHolder,
datasetName, recordedBy, depth, locality, habitat) %>%
distinct()
# check problemas reportados (flags)
flying_obis1 %>%
distinct(flags)## # A tibble: 8 × 1
## flags
## <chr>
## 1 NO_DEPTH
## 2 NO_DEPTH,ON_LAND
## 3 <NA>
## 4 ON_LAND
## 5 ON_LAND,NO_DEPTH
## 6 DEPTH_EXCEEDS_BATH
## 7 MIN_DEPTH_EXCEEDS_MAX,DEPTH_EXCEEDS_BATH
## 8 ON_LAND,DEPTH_EXCEEDS_BATH# check NA em datasetName
flying_obis1 %>%
filter(!flags %in% c("no_depth,on_land", "on_land", "on_land,depth_exceeds_bath", "depth_exceeds_bath,on_land"),
is.na(datasetName)) %>%
distinct(waterBody)## # A tibble: 19 × 1
## waterBody
## <chr>
## 1 <NA>
## 2 Africa
## 3 South America
## 4 Caribbean
## 5 atlantique
## 6 Atlantic
## 7 South Atlantic
## 8 North Atlantic Ocean
## 9 Caribe
## 10 Gulf of Mexico
## 11 North America
## 12 Europe
## 13 Asia
## 14 Atlantic Ocean
## 15 North America Atlantic
## 16 indien
## 17 Central America
## 18 Caribbean Sea
## 19 pacifique# depth ok
flying_obis1 %>%
filter(!flags %in% c("no_depth,on_land", "on_land", "on_land,depth_exceeds_bath", "depth_exceeds_bath,on_land"),
!is.na(datasetName),
!waterBody %in% c("Asia", "indien", "pacifique")) %>%
ggplot(aes(x = depth, fill = waterBody)) +
geom_histogram() 
# checar niveis
flying_obis1 %>%
filter(!flags %in% c("no_depth,on_land", "on_land", "on_land,depth_exceeds_bath", "depth_exceeds_bath,on_land"),
!is.na(datasetName),
!waterBody %in% c("Asia", "indien", "pacifique")) %>%
lapply(., unique)## $scientificName
## [1] "Dactylopterus volitans"
##
## $decimalLatitude
## [1] 46.000000 39.540000 6.883333 39.539000 39.670000 44.979852
## [7] 39.536000 44.957500 39.820000 39.850000 39.529000 41.180000
## [13] -3.850000 49.916666 44.750000 11.273350 37.570000 37.720000
## [19] 39.486000 11.219170 53.267000 39.440000 44.680000 46.833332
## [25] 10.954585 16.885000 43.494000 6.933333 11.833330 41.671800
## [31] 6.483333 33.730000 50.250000 43.716667 45.250000 39.230000
## [37] 53.267002 -22.950000 15.793972 2.950000 39.840000 40.110000
## [43] 50.210797 -23.433333 16.783300 43.049999 40.504333 42.766666
## [49] 43.050000 50.047152 19.800000 43.680000 18.708000 39.310000
## [55] -23.000000 45.020000 49.973620 37.550000 11.207970 42.116665
## [61] -27.500000
##
## $decimalLongitude
## [1] -42.025000 2.330000 -55.433333 2.592700 2.450000 -65.806016
## [7] 2.376600 -45.018900 4.180000 2.740000 2.439300 1.790000
## [13] -33.816667 -5.559220 -65.516700 -74.206970 -0.730000 -0.640000
## [19] 2.477000 -74.240580 -9.045296 3.330000 -63.130000 -53.750000
## [25] -64.145604 -25.000000 -65.468400 -54.983333 2.476800 -66.750000
## [31] 2.802900 -56.200000 -76.900000 -6.333333 -60.500000 -57.800000
## [37] 2.980000 -9.045303 -42.166667 -79.848611 -49.066667 4.090000
## [43] 3.690000 -60.529810 -44.200000 -88.066700 -64.033300 -73.629000
## [49] -64.666700 -5.405545 -91.333300 -65.330000 -91.570000 3.260000
## [55] -42.000000 -65.894400 -6.264514 -0.650000 -74.239700 -65.566700
## [61] -48.500000
##
## $bathymetry
## [1] 4687.0 82.4 49.0 4.0 64.0 84.0 1.0 4438.0 69.6 63.0
## [11] 23.0 25.2 11.0 68.4 -3.0 -26.0 57.4 43.8 12.0 -32.0
## [21] -6.0 51.4 9.0 100.0 -1.0 -2.0 94.0 3.0 11.8 31.0
## [31] 41.0 86.0 56.0 92.0 58.4 28.0 2.0 72.0 74.0 192.0
## [41] 8.0 55.0 50.0 90.0 16.0 110.0 33.0 25.0 -9.0 93.0
## [51] -97.0 87.0 49.4 108.0 22.0 310.0 -87.0
##
## $flags
## [1] "NO_DEPTH" NA "ON_LAND" "ON_LAND,NO_DEPTH"
##
## $waterBody
## [1] NA "Bay of Fundy" "West Atlantic"
## [4] "Mar Caribe" "Atlantic Ocean" "Northwest Atlantic"
## [7] "Terminos Lagoon"
##
## $basisOfRecord
## [1] "PreservedSpecimen" "Occurrence" "MachineObservation"
## [4] "NomenclaturalChecklist" "HumanObservation" "MaterialSample"
##
## $occurrenceStatus
## [1] NA "present" "Present" "Presente"
##
## $rightsHolder
## [1] "The Huntsman Marine Science Centre and Fisheries & Oceans Canada"
## [2] NA
## [3] "Museu Nacional, Universidade Federal do Rio de Janeiro"
## [4] "MoAm SAS | CORPAMAG"
## [5] "Nova Scotia Museum"
## [6] "O'Connor, Shannon Elizabeth"
## [7] "Her Majesty the Queen in right of Canada, as represented by the Minister of Fisheries and Oceans'"
## [8] "INVEMAR"
## [9] "'Her Majesty the Queen in right of Canada, as represented by the Minister of Fisheries and Oceans'"
## [10] "Canadian Museum of Nature"
##
## $datasetName
## [1] "Atlantic Reference Centre"
## [2] "MEDITS-Spain: Demersal and mega-benthic species from the MEDITS (Mediterranean International Trawl Survey) project on the Spanish continental shelf between 1994 and 2010"
## [3] "Fish collection of National Museum of Nature and Science"
## [4] "Global Marine biodiversity data from Seawatchers Marine Citizen Science Platform 1980-2020"
## [5] "Bay of Fundy Species List"
## [6] "Coleção Ictiológica (MNRJ), Museu Nacional(MN), Universidade Federal do Rio de Janeiro(UFRJ)"
## [7] "1743-2010 National Marine Aquarium (NMA) United Kingdom Marine Fish Recording Scheme"
## [8] "CORPAMAG-MOAM-CONTRATO260-2017-ARRECIFES-PASTOS-2018"
## [9] "Marine Recorder Snapshot extract of surveys entered by National Museums Northern Ireland (NMNI)"
## [10] "Nova Scotia Museum - Marine Birds, Mammals, and Fishes"
## [11] "Ictiofauna_de_Lagunas_Costeras_Margarita"
## [12] "Discovery Expedition Biological Reports"
## [13] "Acadia University: Juvenile Fish Assemblages collected in bays along the Atlantic Coast of mainland Nova Scotia during summers of 2005 and 2006"
## [14] "MMM_ALR_FISH"
## [15] "ECNASAP - East Coast North America Strategic Assessment"
## [16] "Biodiversidad íctica de la Isla Cayo Serranilla Expedición Seaflower 2017 Proyecto Colombia BIO"
## [17] "Northern Gulf of St. Lawrence Fishes"
## [18] "University of Kansas Biodiversity Institute Fish Tissue Collection"
## [19] "VIMS NorthEast Area Monitoring and Assessment Program"
## [20] "Fish"
## [21] "University of Kansas Biodiversity Institute Fish Voucher Collection"
## [22] "Terminos Lagoon Fish Occurrence"
## [23] "Marine Recorder Snapshot extract of surveys entered by SeaSearch"
##
## $recordedBy
## [1] NA
## [2] "G.W.Nunan|D.F.Moraes Jr."
## [3] "Contact: info@nmni.com"
## [4] "Pablo_Ramírez_Villarroel"
## [5] "O'Connor, Shannon Elizabeth. 2008"
## [6] "G.W.Nunan"
## [7] "Arturo Acero-Pizarro | Andrea Polanco-Fernández | José-Julian Tavera | Nacor Bolaños-Cubillos"
## [8] "J.A.Oliveira"
## [9] "AMIK-2008"
## [10] "Navio Toko Maru"
## [11] "Brothers, Edward B; Johnson, David S"
## [12] "[irn: 11130]"
## [13] "R/V Oregon II"
## [14] "G.W.Nunan|Décio F.M.Junior"
## [15] "Contact: info@seasearch.org.uk"
## [16] "G.W.Nunan|D.F.Moraes Jr.|W. Bandeira"
##
## $depth
## [1] NA 83.20 49.00 15.00 62.00 11.00 58.00 63.40 10.00 30.00
## [11] 0.00 45.00 47.75 53.20 6.00 9.00 34.00 57.60 64.80 171.57
## [21] 27.00 50.00 26.00 92.14 7.00 52.40 9.50 101.00 165.00
##
## $locality
## [1] "unspecified"
## [2] NA
## [3] "Bay of Fundy"
## [4] "groove on the seaward margin of the reef, off the southern extremity of Ilha do Farol, Atol das Rocas, mun. Natal, RN"
## [5] "\"old fishway\", annapolis tidal generating station, annapolis royal, EBB tide"
## [6] "Bahía de Taganga"
## [7] "Bahía de Gaira"
## [8] "Harricott Beach, St. Mary's Bay"
## [9] "Laguna de Boca de Palo"
## [10] "St Vincent, Cape Verde Islands"
## [11] "Port La Tour"
## [12] "Archipiélago Los Roques"
## [13] "Stratum.54"
## [14] "Prainha, Arraial do Cabo, RJ"
## [15] "Isla Cayo Serranilla"
## [16] "Estação 1999, foz do Amazonas (entre Salinópolis e Cabo Orange), AP"
## [17] "Carrie Bow Cay, plankton net off dock"
## [18] "04"
## [19] "500 km SE of Sable Island (NS)"
## [20] "Gulf of Mexico: Campeche Banks Oregon Sta. 440-445"
## [21] "Lado sudeste da Ilha de Cabo Frio"
## [22] "ilha de Santa Catarina, SC"
##
## $habitat
## [1] NA "Marine" "Formaciones Coralinas"# ok
flying_obis_ok <- flying_obis1 %>%
filter(!flags %in% c("no_depth,on_land", "on_land", "on_land,depth_exceeds_bath", "depth_exceeds_bath,on_land"),
!is.na(datasetName),
!waterBody %in% c("Asia", "indien", "pacifique", NA)) # check
ggplot() +
geom_polygon(data = world, aes(x = long, y = lat, group = group)) +
coord_fixed() +
theme_classic() +
geom_point(data = flying_obis_ok, aes(x = decimalLongitude, y = decimalLatitude, color = waterBody)) +
labs(x = "longitude", y = "latitude", title = expression(italic("Dactylopterus volitans")))
Para finalizar a limpeza dos dados uniremos as ocorrências do GBIF e do OBIS em um único data frame e checaremos duplicatas ou outros problemas.
# unir GBIF e OBIS
# ver diferencas
setdiff(names(flying_gbif_ok), names(flying_obis_ok))## [1] "acceptedScientificName" "issues" "year"setdiff(names(flying_obis_ok), names(flying_gbif_ok))## [1] "bathymetry" "flags"all_data <- bind_rows(flying_gbif_ok %>%
mutate(repo = paste0("gbif", row.names(.))),
flying_obis_ok %>%
mutate(repo = paste0("obis", row.names(.)))) %>%
column_to_rownames("repo") %>%
dplyr::select(decimalLongitude, decimalLatitude, depth, year, habitat) %>%
distinct() %>%
rownames_to_column("occ") %>%
separate(col = "occ", into = c("datasetName", "rn"), sep = 4) %>%
mutate(scientificName = "Dactylopterus volitans") %>%
dplyr::select(-rn)
# mapear ocorrencias
ggplot() +
geom_polygon(data = world, aes(x = long, y = lat, group = group)) +
coord_fixed() +
theme_classic() +
geom_point(data = all_data, aes(x = decimalLongitude, y = decimalLatitude, color = datasetName)) +
#theme(legend.title = element_blank()) +
labs(x = "longitude", y = "latitude", title = expression(italic("Dactylopterus volitans")))
write.csv(all_data, "C:/Users/letic/Documents/Pasta de atividades/Mestrado/Ciência colaborativa/occ_GBIF-OBIS_dac_voli.csv", row.names = FALSE)Análises exploratórias
Vamos explorar nossos dados filtrados!
Uma pergunta que podemos fazer é: Quais são os anos com maior frequência
de ocorrência do peixe-voador?
A maior frequência de ocorrências foi no ano de 2014, enquanto que
nos anos seguintes a frequência é menor, como podemos observar no
histograma abaixo, gerado pela função hist.
hist(all_data$year, xlab = "Anos", ylab = "Frequência", main = "Histograma das ocorrências de Dactylopterus volitans")
Uma outra pergunta que podemos fazer é: Qual é o habitat com maior ocorrência de peixe-voador?
Para respondermos essa questão iremos agrupar as ocorrências por
habitat de acordo com a latitude. Para isso utilizaremos os pacotes
plotly e a função ggplotly, ambos permitem a
criação e visualização de gráficos interativos. No gráfico interativo
que plotamos podemos selecionar ou ocultar variáveis e encontrar
informações mais específicas apenas passando o cursor do mouse por cima
de cada habitat. No gráfico podemos observar que a maioria das
ocorrências estão agrupadas no habitat
Continouns Midium Relief. Por fim a maioria dos habitats
resgistrados nesse data frame está localizado a uma latitude de 25º,
enquanto apenas o habitat Arenal está a -25º, o que sugere
que maioria dos registros do peixe-voador foi feita no norte global.
library(plotly)
cc <- flying_gbif1 %>%
mutate(lat = round(decimalLatitude)) %>%
group_by(lat, habitat) %>%
summarise(occ = length(habitat)) %>%
ggplot(aes(y = occ, x = lat, color = habitat)) +
geom_point() +
theme_classic(base_size = 15) +
labs(x = "Latitude", y = 'Ocorrência')
ggplotly(cc)Considerações finais
Agora temos em mãos um data frame limpo, com dados limpos, alguns gráficos e muitas perguntas ainda não feitas. Aprendemos que as ocorrências do peixe-vaodor concentou-se no ano de 2014 e no habitat Continouns Midium Relief. Descobrimos isso por meio dos dados oriundos do GBIF e do OBIS, que limpamos e começamos a analisar com alguns pacotes e algumas funções e tal qual o multiplicidade de perguntas também são os meios e técnicas para respondê-las.